Skip to main content

Association between microstructural white matter abnormalities and cognitive functioning in patients with type 2 diabetes mellitus: a diffusion tensor imaging study

Abstract

Background

Diffusion tensor imaging (DTI) technique is important for exploring more sensitive imaging-based biomarkers in prevention and early treatment of cognitive dysfunction induced by type 2 diabetes mellitus (DM).

Objectives

To predict early cognitive dysfunction and detection of microstructural white matter changes in patients with type 2 DM by diffusion tensor imaging.

Patients and methods

A case-control study included thirty patients aged ≥ 18 years old of both sexes with type 2 DM and 30 controls. All subjects underwent to Montreal Cognitive Assessment (MoCA) “Arabic version”: to detect mild cognitive impairment (MCI) and diffusion tensor imaging study (DTI).

Results

Mild cognitive impairment is related to type 2 DM (56.7% of diabetic group), reduced fractional anisotropy (FA) values, and elevated mean diffusivity (MD) values were related to cognitive impairment evaluated through Montreal Cognitive Assessment (MoCA) in patients with type 2 DM.

Conclusion

The integrity of the white matter measured using DTI vary in MCI diabetics compared with non-MCI diabetics. Such changes have major implications on the cognitive function.

Introduction

Diabetes mellitus (DM) afflicts nearly 382 million people worldwide, a number that is predicted to reach more than 592 million by the year 2035 [1]. Many studies have also raised concerns about the long-term consequences of poor glycemic control on the impairment of cognitive function [2, 3]. Its effects on higher mental functions are frequently ignored because of the lack of obvious signs and the lack of standard evaluation techniques [4]. Many studies have reported a cognitive impairment in T2DM [5]. And that type 2 diabetes is associated with around a 1.5- to 2.5-fold increase in dementia risk [6, 7]. Bruce et al. found that about 17% of the elderly patients with diabetes had moderate to serious impairments in everyday life tasks, 11% had a cognitive impairment [8]. This cognitive impairment can be due to cerebral white matter conditions, related to cerebrovascular lesions such as lacunar infarctions, hyperintensities of white matter (WMHs), and microstructural lesions [9, 10].

Neuroimaging with diffusion tensor imaging (DTI). It is a process for describing microstructural changes or differences with neuropathology and gives details about the quality of the white matter tracts. DTI calculates the fractional anisotropy [FA], which is a measure of the diffusion directionality and the mean diffusivity [MD], which is a directionally averaged measure of diffusion, FA, and MD values vary from 0 to 1 [11].

Diffusion tensor imaging (DTI) is sensitive for measuring the development of the microstructure and integrity of white matter (WM) fiber tracts during the asymptomatic stage of AD [12, 13]. Fractional anisotropy (FA) and mean diffusivity (MD) describes fiber density, axonal diameter, and myelination in white matter (WM) based on quantitative measure of the degree of diffusion anisotropy [9]. The decreased FA and increased MD in mild cognitive impairment (MCI) are signs of the disruption of WM [14, 15]. Thus, DTI technique is important for exploring more sensitive imaging-based biomarkers in the prevention and early treatment of cognitive dysfunction induced by type 2 DM.

Aim of the work

To estimate the frequency of early cognitive dysfunction and to detect changes in microstructural white matter in patients with type 2 DM by means of diffusion tensor imaging.

Patients and methods

This case-control study was carried out in the Neurology Outpatient Clinic, Neurology Department and MRI unit, Diagnostic and Interventional Radiology Department of Suez Canal University Hospital, Ismailia, Egypt. Thirty adult patients aged ≥ 18 years old of both sexes with type 2 DM were included. Patients with type 1 DM, hypertension, history of cerebrovascular accidents, and dementia were excluded. Another 30 healthy subjects matched by ages and sex were included as a control group.

All the patients and control subjects had undergone the following:

History taking

History taking includes age, type of DM, the duration of DM, and type of treatment.

General examination and full neurological evaluation for identification of focal neurological deficit in patients.

Investigations

A fasting and post prandial blood sugar to confirm diagnosis of diabetes mellitus (FPG ≥ 126 mg/dl (7.0 mmol/l), while post prandial 2-h plasma glucose ≥ 200 mg/dl (11.1 mmol/l) [16]. B-glycated hemoglobin (HbA 1c): those with HbA1c levels of 6.5% or higher have diabetes [17]. The reference ranges were good control less than 7%; moderate control, 7–9%; and poor control, more than 9% [18].

Cognitive testing

Montreal Cognitive Assessment scale (MoCA) [19]. All subjects underwent cognitive assessment using the Arabic translated form of Montreal Cognitive Assessment to confirm the presence of mild cognitive impairment [19]. The MoCA’s original validation analysis proposed a cut-off value of ≥ 26 out of 30 points to distinguish healthy subjects from mild cognitive impairment (MCI) [20].

MR imaging protocol

To get higher signal-to-noise ratio (SNR) and motion-free DTI, we used parallel imaging methods and single-shot spin-echo echo-planar imaging (EPI). Depending on the section numbers the total time for imaging of DTI was 7–9 min, in addition to the standard MR imaging exam time [21].

Acquisition of MR images

A 1.5-T MR unit has been used (Achieva; Philips Medical Systems, The Netherlands). T1-weighted sequences before and after paramagnetic contrast material given intravenously, T2-weighted sequences, and fluid-attenuated inversion recovery (FLAIR) sequences were acquired. Using a single-shot echo-planar image sequence with sensitivity encoding, or SENSE, parallel image scheme (reduction factor, 2), DT image data were obtained. The frame matrix was 128 × 128, with a 220 × 220 mm field of view. Parallel to the anterior commissure —posterior commissure line—were acquired transverse sections of 2.75 mm thickness. A total of 50 sections, without gaps, filled the entire hemisphere and brainstem. Thirty-two separate orientations represented diffusion weighting and the b value was 800 mm2/s. Other parameters for the imaging were echo time = 70 ms, repetition period = 6599–8280 ms, number of acquisitions = two [21].

Data processing

We moved the image data for the diffusion-tensor to an offline workstation (extended MR “EWS” workspace, Philips Medical Systems) which is based on the Continuous Tracking Fiber Assignment (FACT) process. Anisotropy was determined using orientation-independent fractional anisotropy (FA) and diffusion-tensor MR color maps based on the values of FA and the three vector elements were developed. Through linking voxel to voxel with the FACT algorithm, three-dimensional FA was then acquired. The fiber tracking termination threshold values for FA were less than 0.15 and for the trajectory angles between the ellipsoids greater than 27° [21].

Three-dimensional tract reconstruction

We used a multi-region-of-interest (ROI) approach to recreate tracts of interest, which utilize established anatomical knowledge of tract trajectories. Tracking was carried out from all pixels within the brain (“brute force” approach), and tests penetrating the manually identified ROIs were allocated to the different tracts associated with the ROIs. By using multiple ROIs for a tract of interest, we used three types of operations, AND, OR, and NOT, whose preference depended on the trajectory characteristic of each tract. Both FA and MD have been calculated on the following tracts on both sides: Superior longitudinal fasciculus, inferior longitudinal fasciculus, uncinate fasciculus, corpus callosum (splenium), and corpus callosum (Genu) [21].

The study was accepted by the Ethical Committee of Suez Canal Faculty of Medicine. Prior to inclusion in the study, written, informed consent was obtained from all the participants.

Statistical analysis

Using IBM SPSS software package version 20.0, data was fed to the computer and analyzed. (Armonk, NY: Published 2016 by IBM Corp). Using number and percent, qualitative data were represented. The Kolmogorov-Smirnov test had been used to check distribution normality. Quantitative data were represented using the range (minimum and maximum), mean, standard deviation and median. Data were tabulated and statistically analyzed to determine the difference between the groups under study with respect to the different parameters.

Descriptive statistics

For representing both quantitative and qualitative variables, frequencies were used. Mean and standard deviation (± SD) were only used for quantitative variables.

Analytical statistics

Student’s t tests were used to compare two means. ANOVA was used for comparing between more than two groups and followed by post hoc test (Tukey) for pair wise comparison. The chi-square test (Fisher or Monte Carlo) was used to analyze the difference between various proportions at the level of 95%. Mann-Whitney test was used to compare between two groups for not normally distributed quantitative variables p value indicates level of significance [22].

Results

This study included 60 subjects, 30 diabetic patients, and 30 nondiabetic subjects as a control. The age of the diabetic group ranged between 60 and 69 years with a mean (63.57 ± 2.8) and for the control group ranged between 60 and 67 years with a mean (62.77 ± 2.21). Seventeen subjects (56.7%) are males and 13 (43.3%) are females in diabetic group, while in control group, 19 subjects (63.3%) are males and 11 subjects (36.7%) are females. There is no statistically significant difference between both groups regarding age and sex (Table 1).

Table 1 Demographic data of diabetics and controls

Mild Cognitive Impairment (MoCA score: 22–25) is highly significantly more frequent in the diabetics (56.7%) (p < 0.01). The mean value of total MoCA score is highly significantly lower in diabetics (24.87 ± 2.01) than in the non-diabetics (27.90 ± 0.76) (p < 0.01). Cognitive domains in MoCA scale that are mainly affected in the diabetic group are: orientation, short-term memory/delayed recall, executive functions/visuospatial, language, and attention. Abstraction and animal naming show no statistical difference between both groups (Table 2).

Table 2 Montreal Cognitive Assessment scale in diabetics and controls

The mean duration of treatment among diabetic subjects is (3.17 ± 1.27) years. Ten patients (33.3%) are treated for less than 2 years and 20 patients (66.7%) are treated for more than 2 years. Mean values of the duration of treatment in diabetics with MCI (2.77 ± 1.1) years show no significant statistical difference than duration of treatment in diabetics without MCI (3.47 ± 1.33) years (p > 0.05). Moderate and poor glycemic control are significantly more frequent in diabetics with MCI (p < 0.01) (Table 3).

Table 3 Relation between Montreal Cognitive Assessment scale and duration of treatment in diabetic subjects

Fractional anisotropy in diabetic patients with MCI is highly significantly reduced at all selected tracts than diabetic patients without MCI and controls. Mean diffusivity in diabetic patients with MCI is highly significantly elevated at all selected tracts than diabetic patients without MCI and controls (Table 4).

Table 4 Fractional anisotropy and mean diffusivity of controls, diabetics without mild cognitive impairment, and diabetics with mild cognitive impairment

Figure 1 shows the detailed tractography of the right uncinated fasciculus (UNC) in a normal subject. Figure 2 shows the detailed tractography of the right superior longitudinal fasciculus (SLF) in a diabetic subject with mild cognitive impairment.

Fig. 1
figure1

Sagittal view of the detailed tractography of the right uncinated fasciculus (UNC) in a normal subject. Color coding is included to assist with identification of the fibers in the images. Fractional anisotropy (FA) and mean diffusivity (MD) are (0.50 ± 0.140) and (0.61 ± 0.198) respectively

Fig. 2
figure2

Sagittal view of the detailed tractography of the right superior longitudinal fasciculus (SLF) in a diabetic subject with mild cognitive impairment. Color coding is included to assist with identification of the fibers in the images. Fractional anisotropy (FA) is reduced (0.239 ± 0.106) and mean diffusivity is elevated (MD) (0.986 ± 0.236)

Fractional anisotropy in diabetic patients with MCI is highly significantly reduced at all selected tracts (except SLF) at left hemisphere than that at right hemisphere. Mean diffusivity in diabetic patients with MCI is highly significantly elevated at all selected tracts at left hemisphere than at right hemisphere (Table 5).

Table 5 Fractional anisotropy and mean diffusivity at both right and left hemispheres in diabetics with mild cognitive impairment (n = 17)

There was no significant statistical difference between fractional anisotropy and mean diffusivity and duration of treatment in diabetics at all selected tracts at left and right hemispheres (Table 6).

Table 6 Relation between mean values of fractional anisotropy, mean diffusivity and duration of treatment in diabetics (n = 30)

There was significant statistical difference between fractional anisotropy and mean diffusivity and glycemic control in diabetics at all selected tracts at left and right hemispheres except SLF (Table 7).

Table 7 Mean values of fractional anisotropy, mean diffusivity in diabetics according to glycemic control (n = 30)

Discussion

Many studies have built growing questions about mild cognitive impairment (MCI) in T2DM [3, 23,24,25,26]. In our study, 56.7% of the selected diabetic patients (17 patients) has mild cognitive impairment through assessment by MoCA scale and all control group show normal score. This comes in accordance with Naga Swetha and his colleagues (2017) when their results revealed that 57.3% of their selected diabetic patients had MCI [27]. In contrast, in many other studies, MCI prevalence was reported in patients with diabetes mellitus to be between 28 and 38% [28,29,30,31]. This difference may be due to different neuropsychological batteries used and most of those studies was done with young adults and exclude those of age more than 60 years. While the mean of our patients age was (63.57 ± 2.8) years.

In our study, specific cognitive domains of MoCA scale are affected in diabetics with MCI. Orientation, short-term memory, visuospatial/executive functions, language, and attention show significant statistical difference in diabetics with MCI. These findings are in line with those documented by Yue Wang and his colleagues 2015 [32] and Alagiakrishnan and his colleagues (2013) [33]. The cognitive impairment in T2DM is unclear, but hyperglycemia, vascular disease, hypoglycemia, insulin resistance, amyloidosis, concomitant hypertension, and depression could play significant roles [34, 35].

In our study, we picked four tracts of white matter to be tested: superior longitudinal fasciculus, inferior longitudinal fasciculus, uncinate fasciculus, and corpus callosum “both genu and splenium.” Consistent group differences in mean diffusivity values in the majority of tracts in both hemispheres, indicating that individuals with type 2 diabetes have abnormalities in the microstructure of the white matter.

In our study, fractional anisotropy in diabetic patients with MCI is highly significantly reduced at all selected tracts at “both right and left sides” than diabetic patients without MCI and controls. Mean diffusivity in diabetic patients with MCI is highly significantly elevated at all selected tracts at “both right and left sides” than diabetic patients without MCI and controls, FA changes had been less prominent. This can be inerpreted by the fact that the diffusion of white matter fibers is increased parallel (axial) and perpendicular (radial), which has a greater effect on the overall MD than on the axial and radial diffusion (FA) ratio.

Most researches showed a widespread decrease in FA or an rise in MD that could not be interpreted by vascular lesion or total brain volume differences [9, 36,37,38]. The least FA in this study is detected at the splenium of corpus callosum “mainly the left” with mean value 0.33 ± 0.03. This is similar to what is found by many studies confirming that corpus callosum may significantly contribute to the rate of cognitive decline [39,40,41,42]. In contrast, Zhang and his colleagues (2014) reported increase in MD in whole corpus callosum and superior longitudinal fasciculus related to MCI in their selected patients with T2DM [43].

In the majority of tracts in both hemisphere, we found significant group variations in MD values suggesting microstructural abnormalities in the white matter in type 2 diabetes patients. Such findings are consistent with previous reports from a type 2 diabetes study by DTI [9, 37].

We found that there is significant relation between cognitive decline assessed by MoCA scale and FA measured mainly at left SLF (p = 0.01). This matched with Menge and his colleagues who suggested that the FA values of the SLF may be used as a clinical marker of cognitive function [44]. Also, Medina and his colleagues (2006) reported that white matter fibers which are deep in the posterior white matter especially the superior longitudinal fasciculus are influenced in patients with MCI relative to controls [45]. On the other hand, other studies founded no alterations in the corpus callosum of MCI patient [46,47,48].

We also found that there were more reduced FA and increased MD in the tested white matter tracts in left hemisphere than in the right with statsitical significance found between both hemispheres. This is the same as found by Zhang and his colleagues (2014) in their study [43]. This can be demonstrated by the HAROLD model (Hemispheric Asymmetry Reduction in Older Adults) [49] which claimed that decreases in age-related hemispheric asymmetry compensate the lower prefrontal activity lateralization during cognitive performance in older adults compared to younger ones.

In our study, duration of diabetes is not of statistical relation with the cognitive impairment found among diabetics with MCI. Same results was reported by Reijmer and his colleagues (2013) [9]. In contrast, Rosebud Roberts and his colleagues (2008) [50], Chen and his colleagues (2012) [51], and LIU and his colleagues (2017) [52] founded a statistical relation between MCI and duration of diabetes. The discrepancy in the results may be due to variations in the nature of the study, sources of research subjects, and variations in length or intensity of DM between subjects of research.

In our study, there is a statistical significance between the cognitive impairment and the degree of glycemic. We found that 41.2% of the diabetics with MCI were of moderate glycemic control with HbA1c = 7–9% and 58.8% had poor glycemic control with HbA1c > 9%. Several studies have shown that HbA1clevel and cognitive function have a very close relationship [3, 23,24,25,26, 53,54,55,56,57,58,59,60,61]. In the contrary, other studies founded that there is no significant relation between glycemic control and mild cognitive impairment [62, 63]. This discrepancy in confirming the relation between the glycemic control and cognitive decline rises from the point that studies which confirmed that point, were conducted mostly in patients above the mean age of 60 years, as our study.

Conclusion

Mild cognitive impairment is related to T2DM (56.7%). The integrity of the white matter measured using DTI vary in MCI diabetics compared with non-MCI diabetics. Such changes have major implications on the cognitive function. In left hemisphere, the FA values are statistically more reduced and MD values are statistically more elevated.

Availability of data and materials

The data can be publicly available at the Faculty of Medicine, Suez Canal University.

Abbreviations

CCG:

Corpus callosum genu

CCS:

Corpus callosum splenium

DM:

Diabetes mellitus

DTI:

Diffusion tensor imaging

FA:

Fractional anisotropy

MCI:

Mild cognitive impairment

MD:

Mean diffusivity

MoCA:

Montreal Cognitive Assessment scale

ILF:

Inferior longitudinal fasciculus

SLF:

Superior longitudinal fasciculus

UNC:

Uncinate

References

  1. 1.

    Forouhi NG, Wareham NJ. Epidemiology of diabetes. Medicine (United Kingdom). 2019;42(12):698–702.

    Google Scholar 

  2. 2.

    Cheng G, Huang C, Deng H, Wang H. Diabetes as a risk factor for dementia and mild cognitive impairment: a meta-analysis of longitudinal studies. Intern Med J. 2012;42(5):484–91.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  3. 3.

    Cukierman-Yaffe T, Gerstein HC, Williamson JD, Lazar RM, Lovato L, Miller ME, et al. Relationship between baseline glycemic control and cognitive function in individuals with type 2 diabetes and other cardiovascular risk factors: the action to control cardiovascular risk in diabetes-memory in diabetes (ACCORD-MIND) trial. Diabetes Care. 2009;32(2):221–6.

    PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Arvanitakis Z, Wilson RS, Schneider JA, Bienias JL, Evans DA, Bennett DA. Diabetes mellitus and progression of rigidity and gait disturbance in older persons. Neurology. 2004;63(6):996–1001.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  5. 5.

    Biessels GJ, Kappelle AC, Bravenboer B, Erkelens DW, Gispen WH. Cerebral function in diabetes mellitus. Diabetologia. 1994;37(7):643–50.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  6. 6.

    Ott A, Stolk RP, Van Harskamp F, Pols HA, Hofman A, Breteler MM. Diabetes mellitus and the risk of dementia: The Rotterdam Study. Neurology. 1999;53(9):1937–42.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  7. 7.

    Peila R, Rodriguez BL, Launer LJ. Type 2 diabetes, APOE gene, and the risk for dementia and related pathologies: The Honolulu-Asia Aging Study. Diabetes. 2002;51(4):1256–62.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  8. 8.

    Bruce DG, Casey GP, Grange V, Clarnette RC, Almeida OP, Foster JK, et al. Cognitive impairment, physical disability and depressive symptoms in older diabetic patients: the Fremantle Cognition in Diabetes Study. Diabetes Res Clin Pract. 2003;61(1):59–67.

    PubMed  Article  PubMed Central  Google Scholar 

  9. 9.

    Reijmer YD, Brundel M, De Bresser J, Kappelle LJ, Leemans A, Biessels GJ, et al. Microstructural white matter abnormalities and cognitive functioning in type 2 diabetes: a diffusion tensor imaging study. Diabetes Care. 2013;36(1):137–44.

    PubMed  Article  PubMed Central  Google Scholar 

  10. 10.

    Manschot SM, Brands AM, van der Grond J, Kessels RP, Algra A, Kappelle LJ, et al. Brain magnetic resonance imaging correlates of impaired cognition in patients with type 2 diabetes. Diabetes. 2006;55(4):1106–13.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  11. 11.

    Basser PJ, Jones DK. Diffusion-tensor MRI: theory, experimental design and data analysis–a technical review. NMR in Biomedicine: An International Journal Devoted to the Development and Application of Magnetic Resonance In Vivo. 2002;15(7-8):456–67.

    Article  Google Scholar 

  12. 12.

    Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, et al. Diffusion tensor imaging: concepts and applications. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine. 2001;13(4):534–46.

    Article  Google Scholar 

  13. 13.

    Li H, Liang Y, Chen K, Li X, Shu N, Zhang Z, et al. Different patterns of white matter disruption among amnestic mild cognitive impairment subtypes: relationship with neuropsychological performance. J Alzheimers Dis. 2013;36(2):365–76.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Stahl R, Dietrich O, Teipel SJ, Hampel H, Reiser MF, Schoenberg SO. White matter damage in Alzheimer disease and mild cognitive impairment: assessment with diffusion-tensor MR imaging and parallel imaging techniques. Radiology. 2007;243(2):483–92.

    PubMed  Article  PubMed Central  Google Scholar 

  15. 15.

    American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes care. 2010; 33(Supplement 1):S62-9.

  16. 16.

    Khaw KT, Wareham N, Bingham S, Luben R, Welch A, Day N. Preliminary communication: glycated hemoglobin, diabetes, and incident colorectal cancer in men and women: a prospective analysis from the European prospective investigation into Cancer–Norfolk Study. Cancer Epidemiology and Prevention Biomarkers. 2004;13(6):915–9.

    Google Scholar 

  17. 17.

    American Diabetes Association. Standards of medical care in diabetes--2014. Diabetes care. 2014; 37:S14.

  18. 18.

    Rahman TT, El Gaafary MM. Montreal Cognitive Assessment Arabic version: reliability and validity prevalence of mild cognitive impairment among elderly attending geriatric clubs in Cairo. Geriatr Gerontol Int. 2009;9(1):54–61.

    PubMed  Article  PubMed Central  Google Scholar 

  19. 19.

    Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695–9.

    PubMed  Article  PubMed Central  Google Scholar 

  20. 20.

    O’Sullivan M, Morris RG, Huckstep B, Jones DK, Williams SC, Markus HS. Diffusion tensor MRI correlates with executive dysfunction in patients with ischaemic leukoaraiosis. J Neurol Neurosurg Psychiatry. 2004;75(3):441–7.

    PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Ibrahim A, Gomaa M, Sakr H, Elzaher Y. Role of diffusion tensor imaging in characterization and preoperative planning of brain neoplasms. The Egyptian Journal of Radiology and Nuclear Medicine. 2013;44(2):297–307.

    Article  Google Scholar 

  22. 22.

    Krzywinski M, Altman N. Significance, P values and t-tests: the P value reported by tests is a probabilistic significance, not a biological one. Nat Methods. 2013;10(11):1041–3.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  23. 23.

    Yaffe K, Falvey C, Hamilton N, Schwartz AV, Simonsick EM, Satterfield S, et al. Diabetes, glucose control, and 9-year cognitive decline among older adults without dementia. Arch Neurol. 2012;69(9):1170–5.

    PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Meneilly GS, Cheung E, Tessier D, Yakura C, Tuokko H. The effect of improved glycemic control on cognitive functions in the elderly patient with diabetes. J Gerontol. 1993;48(4):M117–21.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  25. 25.

    Biessels GJ, Staekenborg S, Brunner E, Brayne C, Scheltens P. Risk of dementia in diabetes mellitus: a systematic review. The Lancet Neurology. 2006;5(1):64–74.

    PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    Cukierman T, Gerstein HC, Williamson JD. Cognitive decline and dementia in diabetes—systematic overview of prospective observational studies. Diabetologia. 2005;48(12):2460–9.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  27. 27.

    Naga Swetha MS, Kumar VA. Study of Risk Factors of Mild Cognitive Impairment in Patients with Type 2 Diabetes Mellitus. Int J Sci Res Publ. 2017;7(11):672.

    Google Scholar 

  28. 28.

    Luchsinger JA, Reitz C, Patel B, Tang MX, Manly JJ, Mayeux R. Relation of diabetes to mild cognitive impairment. Arch Neurol. 2007;64(4):570–5.

    PubMed  Article  PubMed Central  Google Scholar 

  29. 29.

    Yamazaki Y, Miwa T, Sakurai H, Hanyu H, Iwamoto T, Odawara M. Clinical backgrounds and morbidity of cognitive impairment in elderly diabetic patients. Endocr J. 2011;58(2):109–15.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  30. 30.

    Gorska-Ciebiada M, Saryusz-Wolska M, Ciebiada M, Loba J. Mild cognitive impairment and depressive symptoms in elderly patients with diabetes: prevalence, risk factors, and comorbidity. J Diabetes Res. 2014;2014.

  31. 31.

    Roy S, Kim N, Desai A, Komaragiri M, Baxi N, Jassil N, et al. Cognitive function and control of type 2 diabetes mellitus in young adults. N Am J Med Sci. 2015;7(5):1–7.

    Article  Google Scholar 

  32. 32.

    Wang Y, Xu XY, Feng CH, Li YL, Ge X, Zong GL, et al. Patients with type 2 diabetes exhibit cognitive impairment with changes of metabolite concentration in the left hippocampus. Metab Brain Dis. 2015;30(4):1027–34.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  33. 33.

    Alagiakrishnan K, Zhao N, Mereu L, Senior P, Senthilselvan A. Montreal Cognitive Assessment is superior to Standardized Mini-Mental Status Exam in detecting mild cognitive impairment in the middle-aged and elderly patients with type 2 diabetes mellitus. Biomed Res Int. 2013;2013.

  34. 34.

    Kodl CT, Seaquist ER. Cognitive dysfunction and diabetes mellitus. Endocr Rev. 2008;29(4):494–511.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Cosway R, Strachan MW, Dougall A, Frier BM, Deary IJ. Cognitive function and information processing in type 2 diabetes. Diabet Med. 2001;18(10):803–10.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  36. 36.

    Falvey CM, Rosano C, Simonsick EM, Harris T, Strotmeyer ES, Satterfield S, et al. Macro-and microstructural magnetic resonance imaging indices associated with diabetes among community-dwelling older adults. Diabetes Care. 2013;36(3):677–82.

    PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Hsu JL, Chen YL, Leu JG, Jaw FS, Lee CH, Tsai YF, et al. Microstructural white matter abnormalities in type 2 diabetes mellitus: a diffusion tensor imaging study. Neuroimage. 2012;59(2):1098–105.

    PubMed  Article  PubMed Central  Google Scholar 

  38. 38.

    Yau PL, Javier D, Tsui W, Sweat V, Bruehl H, Borod JC, et al. Emotional and neutral declarative memory impairments and associated white matter microstructural abnormalities in adults with type 2 diabetes. Psychiatry Res Neuroimaging. 2009;174(3):223–30.

    Article  Google Scholar 

  39. 39.

    Dimitra S, Verganelakis DA, Gotsis E, Toulas P, Papatriantafillou J, Karageorgiou C, et al. Diffusion tensor imaging (DTI) in the detection of white matter lesions in patients with mild cognitive impairment (MCI). Acta Neurol Belg. 2013;113(4):441–51.

    PubMed  Article  PubMed Central  Google Scholar 

  40. 40.

    Rose SE, Chen F, Chalk JB, Zelaya FO, Strugnell WE, Benson M, et al. Loss of connectivity in Alzheimer's disease: an evaluation of white matter tract integrity with colour coded MR diffusion tensor imaging. J Neurol Neurosurg Psychiatry. 2000;69(4):528–30.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Cho H, Yang DW, Shon YM, Kim BS, Kim YI, Choi YB, et al. Abnormal integrity of corticocortical tracts in mild cognitive impairment: a diffusion tensor imaging study. J Korean Med Sci. 2008;23(3):477–83.

    PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Takahashi S, Yonezawa H, Takahashi J, Kudo M, Inoue T, Tohgi H. Selective reduction of diffusion anisotropy in white matter of Alzheimer disease brains measured by 3.0 Tesla magnetic resonance imaging. Neurosci Lett. 2002;332(1):45–8.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  43. 43.

    Zhang J, Wang Y, Wang J, Zhou X, Shu N, Wang Y, et al. White matter integrity disruptions associated with cognitive impairments in type 2 diabetic patients. Diabetes. 2014;63(11):3596–605.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  44. 44.

    Meng JZ, Guo LW, Cheng H, Chen YJ, Fang L, Qi M, et al. Correlation between cognitive function and the association fibers in patients with Alzheimer’s disease using diffusion tensor imaging. J Clin Neurosci. 2012;9(12):1659–63.

    Article  Google Scholar 

  45. 45.

    Medina D. deToledo-Morrell L, Urresta F, Gabrieli JD, Moseley M, Fleischman D, et al. White matter changes in mild cognitive impairment and AD: a diffusion tensor imaging study. Neurobiol Aging. 2006;27(5):663–72.

    PubMed  Article  PubMed Central  Google Scholar 

  46. 46.

    Petersen RC, Doody R, Kurz A, Mohs RC, Morris JC, Rabins PV, Ritchie K, Rossor M, Thal L, Winblad B. Current concepts in mild cognitive impairment. Arch Neurol. 2001;58(12):1985–92.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. 47.

    Zhang Y, Schuff N, Jahng GH, Bayne W, Mori S, Schad L, et al. Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology. 2007;68(1):13–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Rose SE, McMahon KL, Janke AL, O’Dowd B, de Zubicaray G, Strudwick MW, et al. Diffusion indices on magnetic resonance imaging and neuropsychological performance in amnestic mild cognitive impairment. J Neurol Neurosurg Psychiatry. 2006;77(10):1122–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Cabeza R. Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychol Aging. 2002;17(1):85.

    PubMed  Article  PubMed Central  Google Scholar 

  50. 50.

    Roberts RO, Geda YE, Knopman DS, Christianson TJ, Pankratz VS, Boeve BF, et al. Association of duration and severity of diabetes mellitus with mild cognitive impairment. Arch Neurol. 2008;65(8):1066–73.

    PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Chen RH, Jiang XZ, Zhao XH, Qin YL, Gu Z, Gu PL, et al. Risk factors of mild cognitive impairment in middle aged patients with type 2 diabetes: A cross–section study. In Annales d'endocrinologie. 2012;73(3):208–12.

    Article  Google Scholar 

  52. 52.

    Liu ZQ, Zhang MX, Wang J, Ding N. Analysis of correlation between the mild cognitive impairment (MCI) and level of adiponectin in elderly patients with type 2 diabetes mellitus (T2DM). Eur Rev Med Pharmacol Sci. 2017;21(23):5471–7.

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    Crane PK, Walker R, Hubbard RA, Li G, Nathan DM, Zheng H, et al. Glucose levels and risk of dementia. N Engl J Med. 2013;369(6):540–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Sanz CM, Ruidavets JB, Bongard V, Marquié JC, Hanaire H, Ferrières J, Andrieu S. Relationship between markers of insulin resistance, markers of adiposity, HbA1c, and cognitive functions in a middle-aged population–based sample: the MONA LISA study. Diabetes Care. 2013;36(6):1512–21.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Umegaki H, Kawamura T, Mogi N, Umemura T, Kanai A, Sano T. Glucose control levels, ischaemic brain lesions, and hyperinsulinaemia were associated with cognitive dysfunction in diabetic elderly. Age Ageing. 2008;37(4):458–61.

    PubMed  Article  PubMed Central  Google Scholar 

  56. 56.

    Shorr RI, De Rekeneire N, Resnick HE, Yaffe K. Glycemia and cognitive function in older adults using glucose-lowering drugs. J Nutr Health Aging. 2006;10(4):297.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Kanaya AM, Barrett-Connor E, Gildengorin G, Yaffe K. Change in cognitive function by glucose tolerance status in older adults: a 4-year prospective study of the Rancho Bernardo study cohort. Arch Intern Med. 2004;164(12):1327–33.

    PubMed  Article  PubMed Central  Google Scholar 

  58. 58.

    Launer LJ, Miller ME, Williamson JD, Lazar RM, Gerstein HC, Murray AM, et al. Effects of intensive glucose lowering on brain structure and function in people with type 2 diabetes (ACCORD MIND): a randomised open-label substudy. The Lancet Neurology. 2011;10(11):969–77.

    PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Nguyen HT, Grzywacz JG, Arcury TA, Chapman C, Kirk JK, Ip EH, et al. Linking glycemic control and executive function in rural older adults with diabetes mellitus. J Am Geriatr Soc. 2010;58(6):1123–7.

    PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Yaffe K, Blackwell T, Whitmer RA, Krueger K, Barrett-Connor E. Glycosylated hemoglobin level and development of mild cognitive impairment or dementia in older women. J Nutr Health Aging. 2006;10(4):293–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Zhang YW, Zhang JQ, Liu C, Wei P, Zhang X, Yuan QY, et al. Memory dysfunction in type 2 diabetes mellitus correlates with reduced hippocampal CA1 and subiculum volumes. Chin Med J. 2015;128(4):465–71.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Ikonomovic MD, Klunk WE, Abrahamson EE, Mathis CA, Price JC, Tsopelas ND, et al. Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer's disease. Brain. 2008;131(6):1630–45.

    PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Van den Berg E, Reijmer YD, De Bresser J, Kessels RP, Kappelle LJ, Biessels GJ, et al. A 4 year follow-up study of cognitive functioning in patients with type 2 diabetes mellitus. Diabetologia. 2010;53(1):58–65.

    PubMed  Article  CAS  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable

Funding

The authors were responsible for the cost of this study including the design of the study and collection, analysis, and interpretation of data and in writing the manuscript and no funding.

Author information

Affiliations

Authors

Contributions

OA carried out the study conception and design, participated in its design and coordination, and drafted the manuscript. HT carried out the design of the study, the analysis, and interpretation of data and helped to draft the manuscript. NM participated in the sequence alignment, interpretation of data, and drafting of manuscript. AR carried out the study conception and design, and participated in its design. AH participated by acquisition of data and performed the statistical analysis. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Reda AbdEl-Razek.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Ethics committee of Suez Canal Faculty of Medicine in 20 January 2016. Committee Number: 2612. An informed written consent was taken from all the participants in the study.

Consent for publication

Participants signed an informed consent for publication.

Competing interests

The authors declare that they have no competing interests (financial or non-financial).

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Osama, A., Khalil, T.H., Negm, M. et al. Association between microstructural white matter abnormalities and cognitive functioning in patients with type 2 diabetes mellitus: a diffusion tensor imaging study. Egypt J Neurol Psychiatry Neurosurg 56, 118 (2020). https://doi.org/10.1186/s41983-020-00232-w

Download citation

Keywords

  • Type 2 DM
  • Mild cognitive impairment
  • Diffusion tensor imaging